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CURIE: Evaluating LLMs On Multitask Scientific Long Context Understanding and Reasoning "We introduce CURIE, a scientific long-Context Understanding, Reasoning and Information Extraction benchmark to measure the potential of Large Language Models (LLMs) in scientific… https://t.co/ErIGSJNpEv
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"text": "CURIE: Evaluating LLMs On Multitask Scientific Long Context Understanding and Reasoning\n\n\"We introduce CURIE, a scientific long-Context Understanding, Reasoning and Information Extraction benchmark to measure the potential of\rLarge Language Models (LLMs) in scientific problem-solving and assisting scientists in realistic workflows. This benchmark introduces ten challenging tasks with\ra total of 580 problems and solution pairs curated by experts in six disciplines - materials science, condensed matter physics, quantum computing, geospatial analysis, biodiversity, and proteins - covering both experimental and theoretical workflows in science. We evaluate a range of closed and open LLMs on tasks in CURIE\rwhich requires domain expertise, comprehension of long in-context information,\rand multi-step reasoning. While Gemini Flash 2.0 and Claude-3 show consistent\rhigh comprehension across domains, the popular GPT-4o and command-R+ fail\rdramatically on protein sequencing tasks. With the best performance at 32% there\ris much room for improvement for all models.\"",
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